
Thesis
Digital Twin technology represents a transformative advancement in modern
engineering design. This thesis explores how digital replicas of physical systems,
continuously updated through real-time sensor data, enable engineers to simulate,
test, and optimize complex structures with unprecedented accuracy. By integrating
AI, IoT, and advanced computational models, Digital Twins significantly reduce
development time, minimize prototyping costs, and enhance predictive
maintenance capabilities. The study highlights real-world applications across
aerospace, automotive, manufacturing, and smart cities while examining the
challenges of data integration, cybersecurity, and system validation. Ultimately,
Digital Twin technology is shown to redefine engineering workflows, enabling
smarter, adaptive, and highly efficient design ecosystems that shape the future of
intelligent engineering.